Computer algorithms discriminate against black and Latino borrowers just as bank loan officers with biases do, according to a new Berkeley study. The researchers found that both online and human lenders earn higher profits by charging the minority borrowers higher rates, compared to white or Asian borrowers with similar credit profiles. "The move away from humans should remove malice forms of discrimination," says business and finance professor Adair Morse, a co-author of the paper. "But we're moving to an era where we're using variables to statistically discriminate against people in lending." Law professor Robert Bartlett, another co-author, says: "Even controlling for credit worthiness, we see discriminatory effects in the rates at which borrowers obtain mortgages." One of the possible explanations for the discrepancy, the researchers say, is that the algorithms are charging higher rates to consumers who don't shop around, and those consumers are most likely to be black or Latino. The algorithms may also be taking into account borrowers' neighborhoods or other characteristics, like high schools or colleges. For more on this, see the researchers' report at the Haas School of Business.